The Information Theoretic Approach to Signal Anomaly Detection for Cognitive Radio

Efficient utilisation and sharing of limited spectrum resources in an autonomous fashion is one of the primary goals of cognitive radio. However, decentralised spectrum sharing can lead to interference scenarios that must be detected and characterised to help achieve the other goal of cognitive radio—reliable service for the end user. Interference events can be treated as unusual and therefore anomaly detection algorithms can be applied for their detection. Two complementary algorithms based on information theoretic measures of statistical distribution divergence and information content are proposed. The first method is applicable to signals with periodic structures and is based on the analysis of Kullback-Leibler divergence. The second utilises information content analysis to detect unusual events. Results from software and hardware implementations show that the proposed algorithms are effective, simple, and capable of processing high-speed signals in real time. Additionally, neither of the algorithms require demodulation of the signal.

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Image Retrieval Using Mutual Information

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1537 ◽

2013 ◽

Vol 427-429 ◽

pp. 1537-1543 ◽

Cited By ~ 2

Author(s):

Ya Fen Wang ◽

Feng Zhen Zhang ◽

Shan Jian Liu ◽

Meng Huang

Keyword(s):

Mutual Information ◽

Image Retrieval ◽

Image Similarity ◽

Theoretic Approach ◽

Information Theoretic ◽

Content Base Image Retrieval ◽

Leibler Divergence ◽

Similar Images ◽

Information Theoretic Approach ◽

The Given

In this paper, we study an information theoretic approach to image similarity measurement for content-base image retrieval. In this novel scheme, similarities are measured by the amount of information the images contained about one another mutual information (MI). The given approach is based on the premise that two similar images should have high mutual information, or equivalently, the querying image should convey high information about those similar to it. The method first generates a set of statistically representative visual patterns and uses the distributions of these patterns as images content descriptors. To measure the similarity of two images, we develop a method to compute the mutual information between their content descriptors. Two images with larger descriptor mutual information are regarded as more similar. We present experimental results, which demonstrate that mutual information is a more effective image similarity measure than those have been used in the literature such as Kullback-Leibler divergence and L2 norms.

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Evaluation of the functional basis using an information theoretic approach

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060409990187 ◽

2010 ◽

Vol 24 (1) ◽

pp. 87-105 ◽

Cited By ~ 14

Author(s):

Chiradeep Sen ◽

Benjamin W. Caldwell ◽

Joshua D. Summers ◽

Gregory M. Mocko

Keyword(s):

Information Content ◽

Consumer Products ◽

Controlled Vocabulary ◽

Theoretic Approach ◽

Web Based ◽

Information Theoretic ◽

Functional Basis ◽

Information Theoretic Approach ◽

Function Models ◽

Key Terms

AbstractA metric for computing the information content of function models in mechanical engineering design is proposed. Function models are graph-based representations used to describe the functionality of engineered artifacts, where the nodes are function verbs and the edges are the objects of action. The functional basis, a controlled vocabulary of these verbs and nouns organized in a three level hierarchy, is intended to support consistent representation of function models. The Design Repository is a Web-based archive of function models of consumer products described with the functional basis. This paper presents the theoretical underpinnings of a metric for the information content of function models, the assumptions required to support it, the definitions of key terms associated with it, and its practical interpretation. Finally, the metric is used to study the usefulness of the functional basis through a series of experiments on function models within the Design Repository. The results of the experiment indicate that the secondary level of the functional basis is the most beneficial to designers, both in terms of information content and information density.

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